Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024 |
| Pages | 42-48 |
| Number of pages | 7 |
| ISBN (electronic) | 9780645832211 |
| Publication status | Published - 2024 |
| Event | 41st International Symposium on Automation and Robotics in Construction, ISARC 2024 - Lille, France Duration: 3 Jun 2024 → 5 Jun 2024 |
Publication series
| Name | Proceedings of the International Symposium on Automation and Robotics in Construction |
|---|---|
| ISSN (electronic) | 2413-5844 |
Abstract
Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
Keywords
- Additive Manufacturing, Image Processing, Neural Network, Printing Robot, Process Control
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Artificial Intelligence
Cite this
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Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024. 2024. p. 42-48 (Proceedings of the International Symposium on Automation and Robotics in Construction).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Inline image-based reinforcement detection for concrete additive manufacturing processes using a convolutional neural network
AU - Lachmayer, Lukas
AU - Dittrich, Lars
AU - Recker, Tobias
AU - Dörrie, Robin
AU - Kloft, Harald
AU - Raatz, Annika
PY - 2024
Y1 - 2024
N2 - Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
AB - Within the scope of additive manufacturing of structural concrete components, the integration of reinforcement provides an inevitable opportunity to enhance the load bearing capacity of the components. Besides the rebar integration itself, ensuring as-planned concrete cover is key to achieve a stable and long-term legally permissible integration. The thickness of the as-built concrete cover however is unpredictably altered during printing by the varying material behaviour of the printed concrete. In addition, the lack of opportunities to anchor reinforcement elements before printing can lead to a displacement of reinforcement during printing. In this publication, we present an approach for determining the position of reinforcement elements within additively manufactured components without post-process measurement steps. During the printing process, RGB images and depth camera data are recorded by a camera mounted to the print head. Subsequently, a neural network is employed to distinguish between reinforcement structures and the deposited material within the coloured image. By overlaying the colour image data with the depth information a 3D point cloud is generated, within which the reinforcement is marked.
KW - Additive Manufacturing
KW - Image Processing
KW - Neural Network
KW - Printing Robot
KW - Process Control
UR - http://www.scopus.com/inward/record.url?scp=85199609362&partnerID=8YFLogxK
U2 - 10.22260/ISARC2024/0007
DO - 10.22260/ISARC2024/0007
M3 - Conference contribution
AN - SCOPUS:85199609362
T3 - Proceedings of the International Symposium on Automation and Robotics in Construction
SP - 42
EP - 48
BT - Proceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024
T2 - 41st International Symposium on Automation and Robotics in Construction, ISARC 2024
Y2 - 3 June 2024 through 5 June 2024
ER -